Evidence map›Paper›PMID 33758865›Full record

ArticlebioRxiv : the preprint server for biology2021

Design and proof-of-concept for targeted phage-based COVID-19 vaccination strategies with a streamlined cold-free supply chain.

Daniela I Staquicini, Fenny H F Tang, Christopher Markosian, Virginia J Yao, Fernanda I Staquicini, Esteban Dodero-Rojas, Vinícius G Contessoto, Deodate Davis, Paul O'Brien, Nazia Habib and 11 more

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

21 authors at 4 institutions in 2 countries.

Daniela I StaquiciniRutgers Cancer Institute of New Jersey, Newark, NJ 07101.
Fenny H F TangRutgers Cancer Institute of New Jersey, Newark, NJ 07101.
Christopher MarkosianRutgers Cancer Institute of New Jersey, Newark, NJ 07101.
Virginia J YaoRutgers Cancer Institute of New Jersey, Newark, NJ 07101.
Fernanda I StaquiciniRutgers Cancer Institute of New Jersey, Newark, NJ 07101.
Esteban Dodero-RojasCenter for Theoretical Biological Physics, Rice University, Houston, TX 77005.
Vinícius G ContessotoCenter for Theoretical Biological Physics, Rice University, Houston, TX 77005.
Deodate DavisRutgers Cancer Institute of New Jersey, Newark, NJ 07101.
Paul O'BrienRutgers Cancer Institute of New Jersey, Newark, NJ 07101.
Nazia HabibRutgers Cancer Institute of New Jersey, Newark, NJ 07101.
Tracey L SmithRutgers Cancer Institute of New Jersey, Newark, NJ 07101.
Natalie BruinersPublic Health Research Institute, Rutgers New Jersey Medical School, Newark, NJ 07103.
Richard L SidmanDepartment of Neurology, Harvard Medical School, Boston, MA 02115.
Maria L GennaroPublic Health Research Institute, Rutgers New Jersey Medical School, Newark, NJ 07103.
Edmund C LattimeRutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901.
Steven K LibuttiRutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901.
Paul C WhitfordDepartment of Physics and Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115.
Stephen K BurleyRutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901.
José N OnuchicCenter for Theoretical Biological Physics, Rice University, Houston, TX 77005.
Wadih ArapRutgers Cancer Institute of New Jersey, Newark, NJ 07101.
Renata PasqualiniRutgers Cancer Institute of New Jersey, Newark, NJ 07101.
Rutgers, The State University of New Jersey · USCenter for Theoretical Biological Physics · USHarvard University · USNortheastern University · US

Funding

TRANSCRIPTIONAL PROFILINGP30CA072720 · NCI · UNIV OF MED/DENT NJ-R W JOHNSON MED SCH · PI Tracie Saunders · 1997 to 2026
$94.5M
PDB MANAGEMENT BY THE RESEARCH COLLABORATORY FOR STRUCTURAL BIOINFORMATICSR01GM133198 · NIGMS · RUTGERS, THE STATE UNIV OF N.J. · PI BURLEY, STEPHEN K · 2019 to 2023
$19.3M
NCI NIH HHS P30 CA072720NIGMS NIH HHS R01 GM133198
6 · The paper itself

Abstract

Development of effective vaccines against Coronavirus Disease 2019 (COVID-19) is a global imperative. Rapid immunization of the world human population against a widespread, continually evolving, and highly pathogenic virus is an unprecedented challenge, and many different vaccine approaches are being pursued to meet this task. Engineered filamentous bacteriophage (phage) have unique potential in vaccine development due to their inherent immunogenicity, genetic plasticity, stability, cost-effectiveness for large-scale production, and proven safety profile in humans. Herein we report the design, development, and initial evaluation of targeted phage-based vaccination approaches against Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) by using dual ligand peptide-targeted phage and adeno-associated virus/phage (AAVP) particles. Towards a unique phage- and AAVP-based dual-display candidate approach, we first performed structure-guided antigen design to select six solvent-exposed epitopes of the SARS-CoV-2 spike (S) protein for display on the recombinant major capsid coat protein pVIII. Targeted phage particles carrying one of these epitopes induced a strong and specific humoral response. In an initial experimental approach, when these targeted phage particles were further genetically engineered to simultaneously display a ligand peptide (CAKSMGDIVC) on the minor capsid protein pIII, which enables receptor-mediated transport of phage particles from the lung epithelium into the systemic circulation (termed "dual-display"), they enhanced a systemic and specific spike (S) protein-specific antibody response upon aerosolization into the lungs of mice. In a second line of investigation, we engineered targeted AAVP particles to deliver the entire S protein gene under the control of a constitutive cytomegalovirus (CMV) promoter, which induced tissue-specific transgene expression stimulating a systemic S protein-specific antibody response. As proof-of-concept preclinical experiments, we show that targeted phage- and AAVP-based particles serve as robust yet versatile enabling platforms for ligand-directed immunization and promptly yield COVID-19 vaccine prototypes for further translational development. SIGNIFICANCE: The ongoing COVID-19 global pandemic has accounted for over 2.5 million deaths and an unprecedented impact on the health of mankind worldwide. Over the past several months, while a few COVID-19 vaccines have received Emergency Use Authorization and are currently being administered to the entire human population, the demand for prompt global immunization has created enormous logistical challenges--including but not limited to supply, access, and distribution--that justify and reinforce the research for additional strategic alternatives. Phage are viruses that only infect bacteria and have been safely administered to humans as antibiotics for decades. As experimental proof-of-concept, we demonstrated that aerosol pulmonary vaccination with lung-targeted phage particles that display short epitopes of the S protein on the capsid as well as preclinical vaccination with targeted AAVP particles carrying the S protein gene elicit a systemic and specific immune response against SARS-CoV-2 in immunocompetent mice. Given that targeted phage- and AAVP-based viral particles are sturdy yet simple to genetically engineer, cost-effective for rapid large-scale production in clinical grade, and relatively stable at room temperature, such unique attributes might perhaps become additional tools towards COVID-19 vaccine design and development for immediate and future unmet needs.

Identifiers

PMID33758865
PMCPMC7987025
OpenAlexW3139465663

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.